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Shaun Purcell

Researcher at Brigham and Women's Hospital

Publications -  347
Citations -  151651

Shaun Purcell is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Genome-wide association study & Population. The author has an hindex of 120, co-authored 326 publications receiving 132973 citations. Previous affiliations of Shaun Purcell include Icahn School of Medicine at Mount Sinai & University of Saint Mary.

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Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder

Manuel A. R. Ferreira, +65 more
- 01 Sep 2008 - 
TL;DR: The results suggest that ion channelopathies may be involved in the pathogenesis of bipolar disorder and found further support for the previously reported CACNA1C.
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Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants.

Sekar Kathiresan, +118 more
- 08 Feb 2009 - 
TL;DR: SNPs at nine loci were reproducibly associated with myocardial infarction, but tests of common and rare CNVs failed to identify additional associations with my Cardiovascular Infarction risk.
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Genome-wide association study identifies 30 loci associated with bipolar disorder

Eli A. Stahl, +342 more
- 01 May 2019 - 
TL;DR: Genome-wide analysis identifies 30 loci associated with bipolar disorder, allowing for comparisons of shared genes and pathways with other psychiatric disorders, including schizophrenia and depression.
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Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Bjarni J. Vilhjálmsson, +394 more
- 01 Oct 2015 - 
TL;DR: LDpred is introduced, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel, and outperforms the approach of pruning followed by thresholding, particularly at large sample sizes.
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A framework for the interpretation of de novo mutation in human disease

Kaitlin E. Samocha, +30 more
- 01 Sep 2014 - 
TL;DR: This model is used to identify ∼1,000 genes that are significantly lacking in functional coding variation in non-ASD samples and are enriched for de novo loss-of-function mutations identified in ASD cases, suggesting that the role of de noVO mutations in ASDs might reside in fundamental neurodevelopmental processes.